{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ZAMZEYQPSYWGLQJY6WY7QMRUT6","short_pith_number":"pith:ZAMZEYQP","schema_version":"1.0","canonical_sha256":"c81992620f962c65c138f5b1f832349fa384c916cc0ee5041c49580592cb3ff4","source":{"kind":"arxiv","id":"2103.12104","version":2},"attestation_state":"computed","paper":{"title":"Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM","physics.ins-det"],"primary_cat":"gr-qc","authors_text":"A K Katsaggelos, B T\\'egl\\'as, C B Jackson, C {\\O}sterlund, C P L Berry, C Unsworth, C Zhang, G Niklasch, K Crowston, K Kaminski, L Trouille, M A Lobato Rodriguez, M Harandi, O Patane, P Nauta, R R Rote, S B Coughlin, S Soni, U Marciniak, V-G Baranowski, W F Domainko","submitted_at":"2021-03-22T18:03:40Z","abstract_excerpt":"The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that a new glitch class linked to ground motion at the detector sites is especially prevalent, and identify two subclasses of this linked to different types of ground motion. Reclassification of da"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2103.12104","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"gr-qc","submitted_at":"2021-03-22T18:03:40Z","cross_cats_sorted":["astro-ph.IM","physics.ins-det"],"title_canon_sha256":"999b41c3df9ca64dbb81f56d6f43965ffcc640bfa8d096c1f507d6adb4ff83ba","abstract_canon_sha256":"330b2e71b13fd74c05c7c0190271e9b15b64bd08cb90bcb776e077ce8d47dd57"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:12:01.759602Z","signature_b64":"vdEEFxvGiZBcHEcfOOm8+QLTqZC4iemoeIBVAUsKaByoHix0G7L0vHmA0QGl4Mm190zmF6shzEms+AByaWe2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c81992620f962c65c138f5b1f832349fa384c916cc0ee5041c49580592cb3ff4","last_reissued_at":"2026-07-05T03:12:01.759145Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:12:01.759145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["astro-ph.IM","physics.ins-det"],"primary_cat":"gr-qc","authors_text":"A K Katsaggelos, B T\\'egl\\'as, C B Jackson, C {\\O}sterlund, C P L Berry, C Unsworth, C Zhang, G Niklasch, K Crowston, K Kaminski, L Trouille, M A Lobato Rodriguez, M Harandi, O Patane, P Nauta, R R Rote, S B Coughlin, S Soni, U Marciniak, V-G Baranowski, W F Domainko","submitted_at":"2021-03-22T18:03:40Z","abstract_excerpt":"The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that a new glitch class linked to ground motion at the detector sites is especially prevalent, and identify two subclasses of this linked to different types of ground motion. Reclassification of da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.12104","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.12104/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2103.12104","created_at":"2026-07-05T03:12:01.759207+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.12104v2","created_at":"2026-07-05T03:12:01.759207+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.12104","created_at":"2026-07-05T03:12:01.759207+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZAMZEYQPSYWG","created_at":"2026-07-05T03:12:01.759207+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZAMZEYQPSYWGLQJY","created_at":"2026-07-05T03:12:01.759207+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZAMZEYQP","created_at":"2026-07-05T03:12:01.759207+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27227","citing_title":"Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27227","citing_title":"Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2508.13923","citing_title":"Hunting for new glitches in LIGO data using community science","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2108.01045","citing_title":"GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run","ref_index":91,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZAMZEYQPSYWGLQJY6WY7QMRUT6","json":"https://pith.science/pith/ZAMZEYQPSYWGLQJY6WY7QMRUT6.json","graph_json":"https://pith.science/api/pith-number/ZAMZEYQPSYWGLQJY6WY7QMRUT6/graph.json","events_json":"https://pith.science/api/pith-number/ZAMZEYQPSYWGLQJY6WY7QMRUT6/events.json","paper":"https://pith.science/paper/ZAMZEYQP"},"agent_actions":{"view_html":"https://pith.science/pith/ZAMZEYQPSYWGLQJY6WY7QMRUT6","download_json":"https://pith.science/pith/ZAMZEYQPSYWGLQJY6WY7QMRUT6.json","view_paper":"https://pith.science/paper/ZAMZEYQP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.12104&json=true","fetch_graph":"https://pith.science/api/pith-number/ZAMZEYQPSYWGLQJY6WY7QMRUT6/graph.json","fetch_events":"https://pith.science/api/pith-number/ZAMZEYQPSYWGLQJY6WY7QMRUT6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZAMZEYQPSYWGLQJY6WY7QMRUT6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZAMZEYQPSYWGLQJY6WY7QMRUT6/action/storage_attestation","attest_author":"https://pith.science/pith/ZAMZEYQPSYWGLQJY6WY7QMRUT6/action/author_attestation","sign_citation":"https://pith.science/pith/ZAMZEYQPSYWGLQJY6WY7QMRUT6/action/citation_signature","submit_replication":"https://pith.science/pith/ZAMZEYQPSYWGLQJY6WY7QMRUT6/action/replication_record"}},"created_at":"2026-07-05T03:12:01.759207+00:00","updated_at":"2026-07-05T03:12:01.759207+00:00"}